Gender: Male 
Age: 60+
Nationality: United Kingdom
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Graduate degree
Mother's education: High school or below
Father's education: High school or below
Self-assessed socioeconomic status (1-10): 8
Home: Own 
Employment: Retired
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer);Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Mother's occupation: Craft related trades workers (those who produce or process foodstuffs, textiles, wooden, metal and other articles, including handicraft goods, and apply specific technical and practical knowledge and skills to construct and maintain buildings)
Father's occupation: Armed forces occupations (includes all jobs held by members of the armed forces)
Hobbies: Visit to stately homes;Go to museums and galleries;Attend football matches;Other (Please specify) Play Chess
Devices used daily: Laptop;Smartphone;Tablet;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools used: Machine translation (e.g. translate Chinese or Russian into English, and vice versa);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools they would find useful: Sentiment analysis (e.g. automatically detect if user reviews are positive or negative) 
Frequency of LLM use: Nearly every day
LLMs used: Google Gemini;ChatGPT;GitHub Copilot 
LLM use cases: Brainstorming;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Solving math or logical problems;Analyze data;Learning (e.g., explaining complex concepts in simple terms);Summarizing long texts 
Contexts of LLM use: Work;Learning;Personal;Entertainment;Technical 
Prompt 1: History of the SAS Rogue Heroes
Prompt 2: What year did Diana Rigg marry Colonel Sterling
Prompt 3: History of British prisoners in Colditz
Prompt 4: Dr Alexander Alekhine and his strange Chess traits
Prompt 5: Ring Magazine, why did they stop sending out their magazines
Prompt 6: The history of the BBBof C
Prompt 7: Libelous claims against Donald Trump
Prompt 8: Worlds highest paid footballer
Prompt 9: Goals compared to each other Cristiano.Ronaldo v Lionel Messi v Pele
Prompt 10: 6 Sigma DPMO Chart
Comments: No, thank you.